| Quantization made by Richard Erkhov. | |
| [Github](https://github.com/RichardErkhov) | |
| [Discord](https://discord.gg/pvy7H8DZMG) | |
| [Request more models](https://github.com/RichardErkhov/quant_request) | |
| InstructProtein - AWQ | |
| - Model creator: https://huggingface.co/hicai-zju/ | |
| - Original model: https://huggingface.co/hicai-zju/InstructProtein/ | |
| Original model description: | |
| --- | |
| license: mit | |
| --- | |
| # InstructProtein | |
| InstructProtein is the first large generative language model exploring the feasibility of bidirectional generation between human and protein language. | |
| It is based on OPT-1.3B architecture with two-step training approach: It initiates with pre-training on protein and natural language corpora, followed by fine-tuning with the established protein knowledge instruction dataset. | |
| Through further instruction tuning, InstructProtein outperforms larger general-purpose foundation models on protein understanding and design tasks. | |
| ## Limitations | |
| The current model, developed through instruction tuning using knowledge instruction dataset, serves as a preliminary example. | |
| Despite its initial success in controlled environments, it lacks the robustness to manage complex, real-world, production-level tasks. | |
| ## Reference | |
| For more information, please take a look at our [paper](https://arxiv.org/abs/2310.03269) and [repository](https://github.com/HICAI-ZJU/InstructProtein). | |